from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__a    :	Optional[int]         =    {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a    :	List[Any]         =    ["""ReformerTokenizer"""]

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a    :	Optional[int]         =    ["""ReformerTokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a    :	List[str]         =    [
		    """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """ReformerAttention""",
		    """ReformerForMaskedLM""",
		    """ReformerForQuestionAnswering""",
		    """ReformerForSequenceClassification""",
		    """ReformerLayer""",
		    """ReformerModel""",
		    """ReformerModelWithLMHead""",
		    """ReformerPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer import ReformerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer_fast import ReformerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_reformer import (
				    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ReformerAttention,
				    ReformerForMaskedLM,
				    ReformerForQuestionAnswering,
				    ReformerForSequenceClassification,
				    ReformerLayer,
				    ReformerModel,
				    ReformerModelWithLMHead,
				    ReformerPreTrainedModel,
				)

else:
		import sys

		__a    :	Optional[int]         =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Union[str, Any]         =    logging.get_logger(__name__)





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)
				if "model" in sd.keys():
								__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)['''model''']

				# pop unnecessary weights
				__lowercase					     =				[
				    '''decoder.version''',
				    '''decoder.output_projection.weight''',
				]
				for key in keys_to_delete:
								if key in sd:
												sd.pop(lowercase				)

				__lowercase					     =				{
				    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
				    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
				    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
				    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
				}
				for old_key, new_key in keys_to_rename.items():
								if old_key in sd:
												__lowercase					     =				sd.pop(lowercase				)

				__lowercase					     =				list(sd.keys()				)
				for key in keys:
								if ".qkv_proj." in key:
												__lowercase					     =				sd[key]
												# We split QKV in separate Q,K,V

												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.q_proj.'''				)
												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.k_proj.'''				)
												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.v_proj.'''				)

												__lowercase					     =				value.shape[0]
												assert depth % 3 == 0
												# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
												# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
												__lowercase     ,	__lowercase     ,	__lowercase					     =				torch.split(lowercase				,     depth // 3				,     dim=0				)

												__lowercase					     =				q
												__lowercase					     =				k
												__lowercase					     =				v
												del sd[key]

				return sd





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase=None				):







				"""simple docstring"""



				__lowercase					     =				load_checkpoint(lowercase				)

				if config is not None:
								__lowercase					     =				OPTConfig.from_pretrained(lowercase				)
				else:
								__lowercase					     =				OPTConfig()

				__lowercase					     =				OPTModel(lowercase				).half().eval()
				model.load_state_dict(lowercase				)

				# Check results
				Path(lowercase				).mkdir(exist_ok=lowercase				)
				model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	int         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--fairseq_path""",
		    type=str,
		    help=(
		        """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
		        """ https://huggingface.co/models?other=opt_metasq"""
		    ),
		)
		parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
		parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
		__a    :	Any         =    parser.parse_args()
		convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)